Sparse quantile-regression selector

A thin wrapper around quantreg::rq.fit.lasso() that always includes an unpenalized intercept and returns a named coefficient vector.

Usage

quantile_lasso_selector(x, y, tau = 0.5, lambda = NULL, ...)

Arguments

x

Numeric design matrix.

y

Numeric response vector.

tau

Quantile level in (0, 1).

lambda

Optional lasso penalty. A scalar applies the same penalty to every slope, while a vector may be supplied either for the slopes alone or for the full coefficient vector including the intercept.

...

Reserved for future selector variants.

Value

A named coefficient vector.